burlap.behavior.singleagent.vfa

Interface ValueFunctionApproximation

All Known Implementing Classes:

LinearFVVFA, LinearVFA

public interface ValueFunctionApproximation

A general interface for defining state value or Q-value function approximation and interacting with it via gradient descent methods.

Author:

James MacGlashan

Method Summary

Methods

Modifier and Type	Method and Description
FunctionWeight	getFunctionWeight(int featureId) Returns the FunctionWeight for the given function's feature id.
java.util.List<ActionApproximationResult>	getStateActionValues(State s, java.util.List<GroundedAction> gas) Returns a state-value (e.g., Q-value) approximation for the query state.
ApproximationResult	getStateValue(State s) Returns a state value approximation for the query state.
WeightGradient	getWeightGradient(ApproximationResult approximationResult) Returns the function weight gradient of the given approximation result.
int	numFeatures() Returns the number of features used in this approximator.
void	resetWeights() Resets the weights as is learning had never been performed.
void	setWeight(int featureId, double w) Sets the weight for a features

Method Detail

getStateValue

ApproximationResult getStateValue(State s)

Returns a state value approximation for the query state.

Parameters:

s - the query state whose state value should be approximated

Returns:

a state value approximation for the query state.

getStateActionValues

java.util.List<ActionApproximationResult> getStateActionValues(State s,
                                                             java.util.List<GroundedAction> gas)

Returns a state-value (e.g., Q-value) approximation for the query state.

Parameters:

s - the query state of the state-action pair to be approximated

gas - the query action of the state-action pair to be approximted

Returns:

a state-value approximation for the query state.

getWeightGradient

WeightGradient getWeightGradient(ApproximationResult approximationResult)

Returns the function weight gradient of the given approximation result.

Parameters:

approximationResult - the approximation result whose weight gradient should be returned

Returns:

the function weight gradient of the given approximation result.

resetWeights

void resetWeights()

Resets the weights as is learning had never been performed.

setWeight

void setWeight(int featureId,
             double w)

Sets the weight for a features

Parameters:

featureId - the feature id whose weight should be set

w - the weight value to use

getFunctionWeight

FunctionWeight getFunctionWeight(int featureId)

Returns the FunctionWeight for the given function's feature id.

Parameters:

featureId - the id of function's feature whose weight is returned.

Returns:

the FunctionWeight for the given function's feature id.

numFeatures

int numFeatures()

Returns the number of features used in this approximator. Note: if features are dynamically added with experience, this number may change with subsequent calls.

Returns:

the number of features used in this approximator.